51
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Gu J, Chersoni E, Wang X, Huang CR, Qian L, Zhou G. LitCovid ensemble learning for COVID-19 multi-label classification. Database (Oxford) 2022; 2022:6846687. [PMID: 36426767 PMCID: PMC9693804 DOI: 10.1093/database/baac103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 10/27/2022] [Accepted: 11/04/2022] [Indexed: 11/27/2022]
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19-related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19-relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative-positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset. Database URL: https://github.com/JHnlp/LCEL.
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Affiliation(s)
| | - Emmanuele Chersoni
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Xing Wang
- Tencent AI Lab, Shenzhen 518071, China
| | - Chu-Ren Huang
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
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Chen Q, Allot A, Leaman R, Wei CH, Aghaarabi E, Guerrerio J, Xu L, Lu Z. LitCovid in 2022: an information resource for the COVID-19 literature. Nucleic Acids Res 2022; 51:D1512-D1518. [PMID: 36350613 PMCID: PMC9825538 DOI: 10.1093/nar/gkac1005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022] Open
Abstract
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.
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Affiliation(s)
| | | | - Robert Leaman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA
| | | | | | | | - Zhiyong Lu
- To whom correspondence should be addressed. Tel: +1 301 594 7089; Fax: +1 301 480 2290;
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53
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Raza S. A COVID-19 Search Engine (CO-SE) with Transformer-based architecture. HEALTHCARE ANALYTICS 2022. [PMID: 37520616 PMCID: PMC9170278 DOI: 10.1016/j.health.2022.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF–IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.
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Lavadi RS, Bozkurt I, Harikar MM, Umana GE, Chaurasia B. The Role of Social Media on the Research Productivity of Neurosurgeons During the COVID-19 Pandemic. World Neurosurg 2022; 167:e1419-e1425. [PMID: 36122854 PMCID: PMC9479383 DOI: 10.1016/j.wneu.2022.09.051] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/11/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND The COVID-19 pandemic committees of all countries restricted face-to-face interactions. This study aimed to determine how the pandemic changed the research output for many neurosurgeons while highlighting how social media may have been used as a contactless platform to maintain research productivity during these times. METHODS A cross-sectional, descriptive, 24-item, and non-randomized online survey was applied worldwide, and shared using social media platforms and emails. The questions mainly focused on comparing the results of the pre-pandemic period to the pandemic period (after March 2020). RESULTS A total of 202 respondents from 60 different countries responded to the survey. Interest in neurosurgical education increased from 24% to 76%, while the topic of epidemiology gained interest from 28% to 72% when the pre-pandemic era was compared to the pandemic era. Preference for prospective studies decreased from 66% to 34%, while interest in retrospective studies increased from 39% to 61%. In evaluating publication types, the preference for reviews increased from 36% to 64%. Sixty-two percent of the respondents stated they had concerns over delays in individual contributions/lack of accountability. These concerns were followed by problems with theft of intellectual property/data and authorship disputes. Forty-one percent believed that the support of extra hands on a load-heavy project was the most powerful benefit of social media collaboration. Those who reported increased publications during the pandemic were also more likely to collaborate using social media (P = 0.030). CONCLUSIONS During the pandemic, social media collaborations helped increase research output for neurosurgeons.
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Affiliation(s)
- Raj Swaroop Lavadi
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA.
| | - Ismail Bozkurt
- Department of Neurosurgery, Cankiri State Hospital, Cankiri, Turkey
| | | | - Giuseppe Emmanuele Umana
- Department of Neurosurgery, Trauma Center, Gamma Knife Center, Cannizzaro Hospital, Catania, Italy
| | - Bipin Chaurasia
- Department of Neurosurgery, Neurosurgery Clinic, Birgunj, Nepal
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Urru S, Sciannameo V, Lanera C, Salaris S, Gregori D, Berchialla P. A topic trend analysis on COVID-19 literature. Digit Health 2022; 8:20552076221133696. [PMID: 36325437 PMCID: PMC9619924 DOI: 10.1177/20552076221133696] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022] Open
Abstract
Objective In the past 2 years, the number of scientific publications has grown exponentially. The COVID-19 outbreak hugely contributed to this dramatic increase in the volume of published research. Currently, text mining of the volume of SARS-CoV-2 and COVID-19 publications is limited to the first months of the outbreak. We aim to identify the major topics in COVID-19 literature collected from several citational sources and analyze the temporal trend from November 2019 to December 2021. Methods We performed an extensive literature search on SARS-Cov-2 and COVID-19 publications on PubMed, Scopus, and Web of Science (WoS) and a structural topic modelling on the retrieved abstracts. The temporal trend of the recognized topics was analyzed. Furthermore, a comparison between our corpus and the COVID-19 Open Research Dataset (CORD-19) repository was performed. Results We collected 269,186 publications and identified 10 topics. The most popular topic was related to the clinical pictures of the COVID-19 outbreak, which has a constant trend, and the least popular includes studies on COVID-19 literature and databases. "Telemedicine", "Vaccine development", and "Epidemiology" were popular topics in the early phase of the pandemic; increasing topics in the last period are "COVID-19 impact on mental health", "Forecasting", and "Molecular Biology". "Education" was the second most popular topic, which emerged in September 2020. Conclusions We identified 10 topics for classifying COVID-19 research publications and estimated a nonlinear temporal trend that gives an overview of their unfolding over time. Several citational databases must be searched to retrieve a complete set of studies despite the efforts to build repositories for COVID-19 literature. Our collected data can help build a more focused literature search between November 2019 and December 2021 when carrying out systematic and rapid reviews and our findings can give a complete picture on the topic.
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Affiliation(s)
- Sara Urru
- Unit of Biostatistics, Epidemiology and Public Health, Department of
Cardiac, Thoracic, Vascular Sciences and Public Health,
University of
Padova, Padua, Italy
| | - Veronica Sciannameo
- Center of Biostatistics, Epidemiology and Public Health, Department
of Clinical and Biological Sciences, University of
Torino, Turin, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of
Cardiac, Thoracic, Vascular Sciences and Public Health,
University of
Padova, Padua, Italy
| | - Silvano Salaris
- Unit of Biostatistics, Epidemiology and Public Health, Department of
Cardiac, Thoracic, Vascular Sciences and Public Health,
University of
Padova, Padua, Italy
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of
Cardiac, Thoracic, Vascular Sciences and Public Health,
University of
Padova, Padua, Italy
| | - Paola Berchialla
- Center of Biostatistics, Epidemiology and Public Health, Department
of Clinical and Biological Sciences, University of
Torino, Turin, Italy,Paola Berchialla, Center of Biostatistics,
Epidemiology and Public Health, Department of Clinical and Biological Sciences,
University of Torino, Regione Gonzole 10, Turin, 10043 Orbassano, Italy.
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56
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He Y, Yu H, Huffman A, Lin AY, Natale DA, Beverley J, Zheng L, Perl Y, Wang Z, Liu Y, Ong E, Wang Y, Huang P, Tran L, Du J, Shah Z, Shah E, Desai R, Huang HH, Tian Y, Merrell E, Duncan WD, Arabandi S, Schriml LM, Zheng J, Masci AM, Wang L, Liu H, Smaili FZ, Hoehndorf R, Pendlington ZM, Roncaglia P, Ye X, Xie J, Tang YW, Yang X, Peng S, Zhang L, Chen L, Hur J, Omenn GS, Athey B, Smith B. A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology. J Biomed Semantics 2022; 13:25. [PMID: 36271389 PMCID: PMC9585694 DOI: 10.1186/s13326-022-00279-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. Results As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. Conclusion CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. Supplementary Information The online version contains supplementary material available at 10.1186/s13326-022-00279-z.
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Affiliation(s)
- Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Hong Yu
- People's Hospital of Guizhou Province, Guiyang, Guizhou, China.
| | | | - Asiyah Yu Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.,National Center for Ontological Research, Buffalo, NY, USA
| | | | - John Beverley
- National Center for Ontological Research, Buffalo, NY, USA.,The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Ling Zheng
- Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhigang Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Yang Wang
- University of Michigan Medical School, Ann Arbor, MI, USA.,People's Hospital of Guizhou Province, Guiyang, Guizhou, China
| | - Philip Huang
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Long Tran
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jinyang Du
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Zalan Shah
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Easheta Shah
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Roshan Desai
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Hsin-Hui Huang
- University of Michigan Medical School, Ann Arbor, MI, USA.,National Yang-Ming University, Taipei, Taiwan
| | - Yujia Tian
- Rutgers University, New Brunswick, NJ, USA
| | | | | | | | - Lynn M Schriml
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jie Zheng
- Department of Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Anna Maria Masci
- Office of Data Science, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | | | | | | | - Robert Hoehndorf
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Zoë May Pendlington
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Paola Roncaglia
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Xianwei Ye
- People's Hospital of Guizhou Province, Guiyang, Guizhou, China
| | - Jiangan Xie
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yi-Wei Tang
- Cepheid, Danaher Diagnostic Platform, Shanghai, China
| | - Xiaolin Yang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Suyuan Peng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luonan Chen
- Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Junguk Hur
- University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA
| | | | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Barry Smith
- National Center for Ontological Research, Buffalo, NY, USA.,University at Buffalo, Buffalo, NY, 14260, USA
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Luo L, Wei CH, Lai PT, Chen Q, Islamaj R, Lu Z. Assigning species information to corresponding genes by a sequence labeling framework. Database (Oxford) 2022; 2022:6760187. [PMID: 36227127 PMCID: PMC9558450 DOI: 10.1093/database/baac090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/26/2022] [Accepted: 10/11/2022] [Indexed: 01/24/2023]
Abstract
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or an identifier by a text-mining algorithm. Existing methods typically rely on heuristic rules based on gene and species co-occurrence in the article, but their accuracy is suboptimal. We therefore developed a high-performance method, using a novel deep learning-based framework, to identify whether there is a relation between a gene and a species. Instead of the traditional binary classification framework in which all possible pairs of genes and species in the same article are evaluated, we treat the problem as a sequence labeling task such that only a fraction of the pairs needs to be considered. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method for the species assignment task (from 65.8-81.3% in accuracy). The source code and data for species assignment are freely available. Database URL https://github.com/ncbi/SpeciesAssignment.
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Affiliation(s)
| | | | - Po-Ting Lai
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Rezarta Islamaj
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- *Corresponding author: Tel: +301 594 7089; Fax: +301 480 2288;
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58
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Chatr-Aryamontri A, Hirschman L, Ross KE, Oughtred R, Krallinger M, Dolinski K, Tyers M, Korves T, Arighi CN. Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII. Database (Oxford) 2022; 2022:6748864. [PMID: 36197453 PMCID: PMC9534061 DOI: 10.1093/database/baac084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/18/2022] [Accepted: 09/08/2022] [Indexed: 11/06/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.
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Affiliation(s)
- Andrew Chatr-Aryamontri
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Marcelle-Coutu Pavilion, 2950 Chem. de Polytechnique Montreal, Quebec H3T 1J4, Canada
| | - Lynette Hirschman
- MITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 2115 Wisconsin Ave NW, DC 20007, USA
| | - Rose Oughtred
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, South Drive, Princeton, NJ 08544, USA
| | - Martin Krallinger
- Barcelona Supercomputing Center (BSC), Plaça d'Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Kara Dolinski
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, South Drive, Princeton, NJ 08544, USA
| | - Mike Tyers
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Marcelle-Coutu Pavilion, 2950 Chem. de Polytechnique Montreal, Quebec H3T 1J4, Canada
| | - Tonia Korves
- MITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA
| | - Cecilia N Arighi
- Computer and Information Sciences Department, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, 590 Avenue 1743, Newark, DE 19713, USA
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An X, Zhang M, Xu S. An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2. PLoS One 2022; 17:e0273725. [PMID: 36112646 PMCID: PMC9480989 DOI: 10.1371/journal.pone.0273725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/13/2022] [Indexed: 11/17/2022] Open
Abstract
To build a full picture of previous studies on the origins of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), this paper exploits an active learning-based approach to screen scholarly articles about the origins of SARS-CoV-2 from many scientific publications. In more detail, six seed articles were utilized to manually curate 170 relevant articles and 300 nonrelevant articles. Then, an active learning-based approach with three query strategies and three base classifiers is trained to screen the articles about the origins of SARS-CoV-2. Extensive experimental results show that our active learning-based approach outperforms traditional counterparts, and the uncertain sampling query strategy performs best among the three strategies. By manually checking the top 1,000 articles of each base classifier, we ultimately screened 715 unique scholarly articles to create a publicly available peer-reviewed literature corpus, COVID-Origin. This indicates that our approach for screening articles about the origins of SARS-CoV-2 is feasible.
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Affiliation(s)
- Xin An
- School of Economics & Management, Beijing Forestry University, Beijing, P.R. China
| | - Mengmeng Zhang
- School of Economics & Management, Beijing Forestry University, Beijing, P.R. China
| | - Shuo Xu
- College of Economics and Management, Beijing University of Technology, Beijing, P.R. China
- * E-mail:
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60
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Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.
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Affiliation(s)
- Xiaotong Wu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Li Mu
- Tianjin Key Laboratory of Agro-environment and Safe-product, Key Laboratory for Environmental Factors Control of Agro-product Quality Safety (Ministry of Agriculture and Rural Affairs), Institute of Agro-environmental Protection, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China.
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Chen Q, Du J, Allot A, Lu Z. LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2584-2595. [PMID: 35536809 PMCID: PMC9647722 DOI: 10.1109/tcbb.2022.3173562] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 05/20/2023]
Abstract
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 200,000 articles with millions of accesses. Approximately 10,000 new articles are added to LitCovid every month. A main curation task in LitCovid is topic annotation where an article is assigned with up to eight topics, e.g., Treatment and Diagnosis. The annotated topics have been widely used both in LitCovid (e.g., accounting for ∼18% of total uses) and downstream studies such as network generation. However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth. This study proposes LITMC-BERT, a transformer-based multi-label classification method in biomedical literature. It uses a shared transformer backbone for all the labels while also captures label-specific features and the correlations between label pairs. We compare LITMC-BERT with three baseline models on two datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the current best results, respectively, and only requires ∼18% of the inference time than the Binary BERT baseline. The related datasets and models are available via https://github.com/ncbi/ml-transformer.
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Chen Q, Allot A, Leaman R, Islamaj R, Du J, Fang L, Wang K, Xu S, Zhang Y, Bagherzadeh P, Bergler S, Bhatnagar A, Bhavsar N, Chang YC, Lin SJ, Tang W, Zhang H, Tavchioski I, Pollak S, Tian S, Zhang J, Otmakhova Y, Yepes AJ, Dong H, Wu H, Dufour R, Labrak Y, Chatterjee N, Tandon K, Laleye FAA, Rakotoson L, Chersoni E, Gu J, Friedrich A, Pujari SC, Chizhikova M, Sivadasan N, VG S, Lu Z. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database (Oxford) 2022; 2022:baac069. [PMID: 36043400 PMCID: PMC9428574 DOI: 10.1093/database/baac069] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/02/2022] [Accepted: 08/13/2022] [Indexed: 05/03/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.
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Affiliation(s)
- Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Alexis Allot
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Robert Leaman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Rezarta Islamaj
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Jingcheng Du
- School of Biomedical Informatics, UT Health, TX, Houston 77030, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shuo Xu
- College of Economics and Management, Beijing University of Technology, Beijing, QC, China
| | - Yuefu Zhang
- College of Economics and Management, Beijing University of Technology, Beijing, QC, China
| | | | | | | | | | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Sheng-Jie Lin
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wentai Tang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Hongtong Zhang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Ilija Tavchioski
- Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
| | | | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Yulia Otmakhova
- School of Computing and Information Systems, University of Melbourne, Melbourne, AU-VIC, Australia
| | | | - Hang Dong
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Honghan Wu
- Institute of Health Informatics, University College London, London, UK
| | | | | | - Niladri Chatterjee
- Department of Mathematics, Indian Institute of Technology Delhi, New Delhi, India
| | - Kushagri Tandon
- Department of Mathematics, Indian Institute of Technology Delhi, New Delhi, India
| | | | | | - Emmanuele Chersoni
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jinghang Gu
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | | | - Subhash Chandra Pujari
- Institute of Computer Science, Heidelberg University, Heidelberg, Germany
- Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Mariia Chizhikova
- SINAI Group, Department of Computer Science, Advanced Studies Center in ICT (CEATIC), Universidad de Jaén, Jaén, Spain
| | | | | | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
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COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study. BIOLOGY 2022; 11:biology11081221. [PMID: 36009848 PMCID: PMC9404775 DOI: 10.3390/biology11081221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/13/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022]
Abstract
Simple Summary The COVID-19-related literature has known a surge since the beginning of the pandemic. This surge prompted the creation of multiple literature exploration systems to help automate the exploration of scientific articles. In this work, we survey multiple COVID-19 literature exploration systems by exploring their most discriminative characteristics, give general design principles for these systems, and describe some of their limitations. Abstract The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used.
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Erdengasileng A, Han Q, Zhao T, Tian S, Sui X, Li K, Wang W, Wang J, Hu T, Pan F, Zhang Y, Zhang J. Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification. Database (Oxford) 2022; 2022:6664140. [PMID: 35962559 PMCID: PMC9375052 DOI: 10.1093/database/baac066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 11/19/2022]
Abstract
Large volumes of publications are being produced in biomedical sciences nowadays with ever-increasing speed. To deal with the large amount of unstructured text data, effective natural language processing (NLP) methods need to be developed for various tasks such as document classification and information extraction. BioCreative Challenge was established to evaluate the effectiveness of information extraction methods in biomedical domain and facilitate their development as a community-wide effort. In this paper, we summarize our work and what we have learned from the latest round, BioCreative Challenge VII, where we participated in all five tracks. Overall, we found three key components for achieving high performance across a variety of NLP tasks: (1) pre-trained NLP models; (2) data augmentation strategies and (3) ensemble modelling. These three strategies need to be tailored towards the specific tasks at hands to achieve high-performing baseline models, which are usually good enough for practical applications. When further combined with task-specific methods, additional improvements (usually rather small) can be achieved, which might be critical for winning competitions. Database URL: https://doi.org/10.1093/database/baac066
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Affiliation(s)
| | - Qing Han
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Tingting Zhao
- Department of Geography, Florida State University , Tallahassee, FL 32306, USA
| | - Shubo Tian
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Xin Sui
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Keqiao Li
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Wanjing Wang
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Jian Wang
- Cloudmedx Inc , Palo Alto, CA 94301, USA
| | - Ting Hu
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Feng Pan
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Yuan Zhang
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University , Tallahassee, FL 32306, USA
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Post-acute COVID-19 syndrome and its prolonged effects: An updated systematic review. Ann Med Surg (Lond) 2022; 80:103995. [PMID: 35721785 PMCID: PMC9197790 DOI: 10.1016/j.amsu.2022.103995] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/08/2022] [Accepted: 06/12/2022] [Indexed: 01/09/2023] Open
Abstract
Objective This systematic review aimed at estimating the prevalence of post-acute COVID-19 symptoms in view of published literature that studied prolonged clinical manifestations after recovery from acute COVID-19 infection. Methods Relevant databases were searched for extraction of articles. For data synthesis, based on the distribution of quantitative variables, they were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR). Qualitative variables were presented as frequency (n) and percentages (%). Results Twenty-one articles qualified for the final analysis. The most common persistent clinical manifestations were fatigue (54.11%), dyspnea (24.38%), alopecia (23.21%), hyperhidrosis (23.6%), insomnia (25.98%), anxiety (17.29%), and arthralgia (16.35%). In addition to these symptoms, new-onset hypertension, diabetes, neuropsychiatric disorders, and bladder incontinence were also reported. Conclusion Clinical features of post-acute COVID-19 infection can manifest even after 60 days of initial infection. Multidisciplinary care along with regular follow-up must be provided to such patients. Clinical features of post-acute COVID-19 infection can manifest even after 60 days of initial infection. Prolonged symptoms and signs are being reported in observational studies and case reports every day. Although such symptoms are usually experienced in survivors of critical illness, the post-acute effects of COVID-19 are equally being reported in patients with mild severity of disease who do not require hospitalization. Necessary future research includes stratification of these post-acute effects with gender, age, and comorbid conditions in acute, subacute, and chronic phases of the disease.
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Vo AT, Patton T, Peacock A, Larney S, Borquez A. Illicit Substance Use and the COVID-19 Pandemic in the United States: A Scoping Review and Characterization of Research Evidence in Unprecedented Times. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148883. [PMID: 35886734 PMCID: PMC9317093 DOI: 10.3390/ijerph19148883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 02/01/2023]
Abstract
We carried out a scoping review to characterize the primary quantitative evidence addressing changes in key individual/structural determinants of substance use risks and health outcomes over the first two waves of the COVID-19 pandemic in the United States (US). We systematically queried the LitCovid database for US-only studies without date restrictions (up to 6 August 2021). We extracted quantitative data from articles addressing changes in: (a) illicit substance use frequency/contexts/behaviors, (b) illicit drug market dynamics, (c) access to treatment and harm reduction services, and (d) illicit substance use-related health outcomes/harms. The majority of 37 selected articles were conducted within metropolitan locations and leveraged historical timeseries medical records data. Limited available evidence supported changes in frequency/behaviors/contexts of substance use. Few studies point to increases in fentanyl and reductions in heroin availability. Policy-driven interventions to lower drug use treatment thresholds conferred increased access within localized settings but did not seem to significantly prevent broader disruptions nationwide. Substance use-related emergency medical services’ presentations and fatal overdose data showed a worsening situation. Improved study designs/data sources, backed by enhanced routine monitoring of illicit substance use trends, are needed to characterize substance use-related risks and inform effective responses during public health emergencies.
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Affiliation(s)
- Anh Truc Vo
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
- Correspondence:
| | - Thomas Patton
- Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, San Diego, CA 92093, USA; (T.P.); (A.B.)
| | - Amy Peacock
- National Drug & Alcohol Research Centre, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Sarah Larney
- Department of Family Medicine and Emergency Medicine, University of Montreal, Montreal, QC H3C 3J7, Canada;
| | - Annick Borquez
- Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, San Diego, CA 92093, USA; (T.P.); (A.B.)
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67
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Lin SJ, Yeh WC, Chiu YW, Chang YC, Hsu MH, Chen YS, Hsu WL. A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. Database (Oxford) 2022; 2022:6645124. [PMID: 35849027 PMCID: PMC9290865 DOI: 10.1093/database/baac056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/20/2022] [Accepted: 07/02/2022] [Indexed: 11/25/2022]
Abstract
In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system’s performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.
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Affiliation(s)
- Sheng-Jie Lin
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Wen-Chao Yeh
- Institute of Information Systems and Applications, National Tsing Hua University, No. 101, Section 2, Guangfu Rd, East District , Hsinchu City 300, Taiwan
| | - Yu-Wen Chiu
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
- Pervasive AI Research Labs, Ministry of Science and Technology, No. 1001, Daxue Rd, East District , Hsinchu City 300, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Yi-Shin Chen
- Institute of Information Systems and Applications, National Tsing Hua University, No. 101, Section 2, Guangfu Rd, East District , Hsinchu City 300, Taiwan
| | - Wen-Lian Hsu
- Pervasive AI Research Labs, Ministry of Science and Technology, No. 1001, Daxue Rd, East District , Hsinchu City 300, Taiwan
- Department of Computer Science and Information Engineering, Asia University, No. 500, Liufeng Rd, Wufeng District , Taichung City 413, Taiwan
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Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2022. [DOI: 10.3390/make4030030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially. However, it is almost impossible for humans to read all of the studies and classify them. This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model. We used the CORD-19 dataset in conjunction with the LitCovid database to construct new vocabulary and prepare the test dataset. For NLI downstream task, we used three corpora: SNLI, MultiNLI, and MedNLI. We significantly reduced the training time by 98.2639% to build a task-specific machine learning model, using only one Nvidia Tesla V100. The final model can run faster and use fewer resources than its comparators. It has an accuracy of 27.84%, which is lower than the best-achieved accuracy by 6.73%, but it is comparable. Finally, we identified that the tokenizer and vocabulary more specific to COVID-19 could not outperform the generalized ones. Additionally, it was found that BART architecture affects the classification results.
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Pacetti G, Baronc-Adesi F, Corvini G, D'Anna C, Schmid M. Use of a modified SIR-V model to quantify the effect of vaccination strategies on hospital demand during the Covid-19 pandemic. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4695-4699. [PMID: 36086252 DOI: 10.1109/embc48229.2022.9871957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A novel compartmental model that includes vaccination strategy, permanence in hospital wards and tracing of infected individuals has been implemented to forecast hospital overload caused by COVID-19 pandemics in Italy. The model parameters were calibrated according to available data on cases, hospital admissions, and number of deaths in Italy during the second wave, and were validated in the timeframe corresponding to the first successive wave where vaccination campaign was fully operational. This model allowed quantifying the decrease of hospital demand in Italy associated with the vaccination campaign. Clinical relevance This study provides evidence for the ability of deterministic SIR-based models to accurately forecast hospital demand dynamics, and support informed decisions regarding dimensioning of hospital personnel and technologies to respond to large-scale epidemics, even when vaccination campaigns are available.
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Langnickel L, Darms J, Heldt K, Ducks D, Fluck J. Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID. Database (Oxford) 2022; 2022:6625658. [PMID: 35776071 PMCID: PMC9248388 DOI: 10.1093/database/baac048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/20/2022] [Accepted: 06/07/2022] [Indexed: 11/12/2022]
Abstract
Abstract
preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has demanded dynamic development. Whereas new semantic concepts have been added over time—such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest—the service has been also extended with several features improving the usability and user friendliness. Most importantly, the user is now able to give feedback on detected semantic concepts, i.e. a user can mark annotations as true positives or false positives. In addition, we expanded our methods to construct search queries. The presented version of preVIEW also includes links to the peer-reviewed journal articles, if available. With the described system, we participated in the BioCreative VII interactive text-mining track and retrieved promising user-in-the-loop feedback. Additionally, as the occurrence of long-term symptoms after an infection with the virus SARS-CoV-2—called long COVID—is getting more and more attention, we have recently developed and incorporated a long COVID classifier based on state-of-the-art methods and manually curated data by experts. The service is freely accessible under https://preview.zbmed.de
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Affiliation(s)
- Lisa Langnickel
- ZB MED - Information Centre for Life Sciences , Gleueler Stra β e 60, 50931 Cologne, Germany
- Faculty of Technology, University of Bielefeld Graduate School DILS Bielefeld Institute for Bioinformatics Infrastructure (BIBI), , Germany
| | - Johannes Darms
- ZB MED - Information Centre for Life Sciences , Gleueler Stra β e 60, 50931 Cologne, Germany
| | - Katharina Heldt
- Robert Koch Institute , Burgstra β e 37, 38855 Wernigerode, Germany
| | - Denise Ducks
- Robert Koch Institute , Burgstra β e 37, 38855 Wernigerode, Germany
| | - Juliane Fluck
- ZB MED - Information Centre for Life Sciences , Gleueler Stra β e 60, 50931 Cologne, Germany
- University of Bonn , Katzenburgweg 1a, 53115 Bonn, Germany
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71
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Gu J, Xiang R, Wang X, Li J, Li W, Qian L, Zhou G, Huang CR. Multi-probe attention neural network for COVID-19 semantic indexing. BMC Bioinformatics 2022; 23:259. [PMID: 35768777 PMCID: PMC9241329 DOI: 10.1186/s12859-022-04803-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 06/15/2022] [Indexed: 11/25/2022] Open
Abstract
Background The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain. Results In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing. Conclusion The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.
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Affiliation(s)
- Jinghang Gu
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | - Rong Xiang
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | | | - Jing Li
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wenjie Li
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Chu-Ren Huang
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China.
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Wan M, Su Q, Xiang R, Huang CR. Data-driven analytics of COVID-19 'infodemic'. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022; 15:313-327. [PMID: 35730040 PMCID: PMC9194350 DOI: 10.1007/s41060-022-00339-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 04/30/2022] [Indexed: 12/02/2022]
Abstract
The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people’s sympathy toward the vulnerable community and foment their spreading behavior.
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Affiliation(s)
- Minyu Wan
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | - Qi Su
- School of Foreign Languages, Peking University, Beijing, China
| | - Rong Xiang
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chu-Ren Huang
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
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Raza S, Schwartz B, Rosella LC. CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice. BMC Bioinformatics 2022; 23:210. [PMID: 35655148 PMCID: PMC9160513 DOI: 10.1186/s12859-022-04751-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time. Methods This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics. Results Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%. Conclusion CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04751-6.
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Lee AS, Balakrishnan IDD, Khoo CY, Ng CT, Loh JK, Chan LL, Teo LL, Sim DK. Myocarditis Following COVID-19 Vaccination: A Systematic Review (October 2020-October 2021). Heart Lung Circ 2022; 31:757-765. [PMID: 35227610 PMCID: PMC8874750 DOI: 10.1016/j.hlc.2022.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/06/2022] [Accepted: 02/01/2022] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Reports of SARS-CoV-2 coronavirus (COVID-19) vaccine-related myocarditis, particularly after mRNA vaccines, have raised concerns amongst the general public. This review examined the literature regarding myocarditis post COVID-19 vaccination, drawing from vaccine safety surveillance databases and case reports. METHODS Combinations of search terms were used in PubMed and COVID-19-specific repositories - LitCovid and the Cochrane COVID-19 Study Register - between 1 October 2020 and 31 October 2021. Manual searches of GoogleScholar and screening of article bibliographies were also performed. RESULTS Information was obtained from five vaccine safety surveillance databases. Fifty-two (52) case reports totalling 200 cases of possible COVID-19 vaccine-related myocarditis were summarised. Vaccine surveillance databases differed in reporting formats and vaccination rates; however, gross estimates suggested low overall incidence rates of 2-5 per million mRNA vaccines. The incidence appeared to be higher in younger male populations, with onset of symptoms within a few days, usually after the second dose. Some with prior COVID-19 infections had onset after the first dose. Cases with prior unrelated myocarditis were also noted. Almost all presented with chest pain (98.0%). Troponin elevation was universally described and cardiac magnetic resonance imaging was commonly reported based on the updated Lake Louise criteria. Clinical course was mild in the majority, with response to anti-inflammatory treatment. CONCLUSION COVID-19 vaccine-related myocarditis is an important but rare adverse event. More research is needed into its pathogenesis and reasons for its predominance in young males, while gaps in data exist in those aged <16 years, as well as those with prior COVID-19 infections and prior myocarditis.
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Affiliation(s)
- Audry S.Y. Lee
- Corresponding author at: National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609
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75
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Jiang C, Ngo V, Chapman R, Yu Y, Liu H, Jiang G, Zong N. Deep Denoising of Raw Biomedical Knowledge Graph from COVID-19 Literature, LitCovid and Pubtator. J Med Internet Res 2022; 24:e38584. [PMID: 35658098 PMCID: PMC9301549 DOI: 10.2196/38584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/05/2022] Open
Abstract
Background Multiple types of biomedical associations of knowledge graphs, including COVID-19–related ones, are constructed based on co-occurring biomedical entities retrieved from recent literature. However, the applications derived from these raw graphs (eg, association predictions among genes, drugs, and diseases) have a high probability of false-positive predictions as co-occurrences in the literature do not always mean there is a true biomedical association between two entities. Objective Data quality plays an important role in training deep neural network models; however, most of the current work in this area has been focused on improving a model’s performance with the assumption that the preprocessed data are clean. Here, we studied how to remove noise from raw knowledge graphs with limited labeled information. Methods The proposed framework used generative-based deep neural networks to generate a graph that can distinguish the unknown associations in the raw training graph. Two generative adversarial network models, NetGAN and Cross-Entropy Low-rank Logits (CELL), were adopted for the edge classification (ie, link prediction), leveraging unlabeled link information based on a real knowledge graph built from LitCovid and Pubtator. Results The performance of link prediction, especially in the extreme case of training data versus test data at a ratio of 1:9, demonstrated that the proposed method still achieved favorable results (area under the receiver operating characteristic curve >0.8 for the synthetic data set and 0.7 for the real data set), despite the limited amount of testing data available. Conclusions Our preliminary findings showed the proposed framework achieved promising results for removing noise during data preprocessing of the biomedical knowledge graph, potentially improving the performance of downstream applications by providing cleaner data.
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Affiliation(s)
| | - Victoria Ngo
- University of California Davis Health, Sacramento, US
| | | | - Yue Yu
- Mayo Clinic, Rochester, US
| | | | | | - Nansu Zong
- Mayo Clinic, 205 3rd Ave SW, Rochester, US
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76
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Farrell MJ, Brierley L, Willoughby A, Yates A, Mideo N. Past and future uses of text mining in ecology and evolution. Proc Biol Sci 2022; 289:20212721. [PMID: 35582795 PMCID: PMC9114983 DOI: 10.1098/rspb.2021.2721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.
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Affiliation(s)
- Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - Liam Brierley
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Anna Willoughby
- Odum School of Ecology, University of Georgia, Athens, GA, USA,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Andrew Yates
- University of Amsterdam, Amsterdam, The Netherlands
| | - Nicole Mideo
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
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Volkov BB, Ragon B, Holmes K, Samuels E, Walden A, Herzog K. Leadership and administration to advance translational science: Environmental scan of adaptive capacity and preparedness of Clinical and Translational Science Award Program hubs. J Clin Transl Sci 2022; 7:e6. [PMID: 36755532 PMCID: PMC9879905 DOI: 10.1017/cts.2022.409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/07/2022] Open
Abstract
COVID-19 reinforced the need for effective leadership and administration within Clinical and Translational Science Award (CTSA) program hubs in response to a public health crisis. The speed, scale, and persistent evolution of the pandemic forced CTSA hubs to act quickly and remain nimble. The switch to virtual environments paired with supporting program operations, while ensuring the safety and well-being of their team, highlight the critical support role provided by leadership and administration. The pandemic also illustrated the value of emergency planning in supporting organizations' ability to quickly pivot and adapt. Lessons learned from the pandemic and from other cases of adaptive capacity and preparedness can aid program hubs in promoting and sustaining the overall capabilities of their organizations to prepare for future events.
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Affiliation(s)
- Boris B. Volkov
- University of Minnesota, Clinical and Translational Science Institute, Minneapolis, MN, USA
- Institute for Health Informatics and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Bart Ragon
- Integrated Translational Health Research Institute of Virginia, University of Virginia, Charlottesville, VA, USA
- University of Virginia, Charlottesville, VA, USA
| | - Kristi Holmes
- Northwestern University Clinical and Translational Sciences Institute (NUCATS), Northwestern University, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Elias Samuels
- Michigan Institute for Clinical and Health Research, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Anita Walden
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR, USA
| | - Keith Herzog
- Northwestern University Clinical and Translational Sciences Institute (NUCATS), Northwestern University, Chicago, IL, USA
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Verdugo-Paiva F, Vergara C, Ávila C, Castro J, Cid J, Contreras V, Jara I, Jiménez V, Lee MH, Muñoz M, Rojas-Gómez AM, Rosón-Rodríguez P, Serrano-Arévalo K, Silva-Ruz I, Vásquez-Laval J, Zambrano-Achig P, Zavadzki G, Rada G. COVID-19 L·OVE repository is highly comprehensive and can be used as a single source for COVID-19 studies. J Clin Epidemiol 2022; 149:195-202. [PMID: 35597369 PMCID: PMC9116966 DOI: 10.1016/j.jclinepi.2022.05.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 04/05/2022] [Accepted: 05/04/2022] [Indexed: 11/15/2022]
Abstract
Background and Objective The coronavirus disease 2019 Living OVerview of Evidence (COVID-19 L·OVE) is a public repository and classification platform for COVID-19 articles. The repository contains more than 430,000 articles as of September 20, 2021 and intends to provide a one-stop shop for COVID-19 evidence. Considering that systematic reviews conduct high-quality searches, this study assesses the comprehensiveness and currency of the repository against the total number of studies in a representative sample of COVID-19 systematic reviews. Methods Our sample was generated from all the studies included in the systematic reviews of COVID-19 published during April 2021. We estimated the comprehensiveness of COVID-19 L·OVE repository by determining how many of the individual studies in the sample were included in the COVID-19 L·OVE repository. We estimated the currency as the percentage of studies that was available in the COVID-19 L·OVE repository at the time the systematic reviews conducted their own search. Results We identified 83 eligible systematic reviews that included 2,132 studies. COVID-19 L·OVE had an overall comprehensiveness of 99.67% (2,125/2,132). The overall currency of the repository, that is, the proportion of articles that would have been obtained if the search of the reviews was conducted in COVID-19 L·OVE instead of searching the original sources, was 96.48% (2,057/2,132). Both the comprehensiveness and the currency were 100% for randomized trials (82/82). Conclusion The COVID-19 L·OVE repository is highly comprehensive and current. Using this repository instead of traditional manual searches in multiple databases can save a great amount of work to people conducting systematic reviews and would improve the comprehensiveness and timeliness of evidence syntheses. This tool is particularly important for supporting living evidence synthesis processes.
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Affiliation(s)
| | - C Vergara
- Epistemonikos Foundation, Santiago, Chile
| | - C Ávila
- Epistemonikos Foundation, Santiago, Chile
| | - J Castro
- Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - J Cid
- School of Dentistry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - I Jara
- Epistemonikos Foundation, Santiago, Chile
| | - V Jiménez
- Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - M H Lee
- Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - M Muñoz
- Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - A M Rojas-Gómez
- Epistemonikos Foundation, Santiago, Chile; Unidad de investigación en medicina estomatológica preventiva y social (UIMEPS), Universidad del Magdalena, Santa Marta, Colombia
| | | | | | - I Silva-Ruz
- School of Dentistry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - G Zavadzki
- School of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | - G Rada
- Epistemonikos Foundation, Santiago, Chile; UC Evidence Center, Cochrane Chile Associated Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Internal Medicine Department, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Siddiqui S, Alhamdi HWS, Alghamdi HA. Recent Chronology of COVID-19 Pandemic. Front Public Health 2022; 10:778037. [PMID: 35602161 PMCID: PMC9114873 DOI: 10.3389/fpubh.2022.778037] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/16/2022] [Indexed: 12/12/2022] Open
Abstract
COVID-19 is highly contagious and is caused by severe acute respiratory syndrome coronavirus 2. It spreads by means of respiratory droplets and close contact with infected persons. With the progression of disease, numerous complications develop, particularly among persons with chronic illnesses. Pathological investigations indicate that it affects multiple organs and can induce acute respiratory distress syndrome. Prevention is vital and self-isolation is the best means of containing this virus. Good community health practices like maintaining sufficient distance from other people, wearing protective face masks and regular hand washing should be adopted. Convalescent plasma transfusion and the administration of the antiviral Remdesivir have been found to be effective. Vaccines offer lifesaving protecting against COVID-19 which has killed millions and our best bet for staying safe. Screening, suppression/containment as well as mitigation are the strategies implemented for controlling COVID-19 pandemic. Vaccination is essential to end the COVID-19 pandemic and everyone should have an access to them. The current COVID-19 pandemic brought the global economy to a standstill and has exacted an enormous human and financial toll.
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Affiliation(s)
- Sazada Siddiqui
- Department of Biology, College of Science, King Khalid University, Abha, Saudi Arabia
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80
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Batra P. Equity in Research Dissemination. JOURNAL OF INDIAN ORTHODONTIC SOCIETY 2022. [DOI: 10.1177/03015742221095308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Puneet Batra
- Editor, Manav Rachna Dental College, Faridabad, India
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81
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Yu W, Drzymalla E, Gwinn M, Khoury MJ. COVID-19 GPH: tracking the contribution of genomics and precision health to the COVID-19 pandemic response. BMC Infect Dis 2022; 22:402. [PMID: 35468755 PMCID: PMC9035978 DOI: 10.1186/s12879-022-07219-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/28/2022] [Indexed: 01/14/2023] Open
Abstract
The scientific response to the COVID-19 pandemic has produced an abundance of publications, including peer-reviewed articles and preprints, across a wide array of disciplines, from microbiology to medicine and social sciences. Genomics and precision health (GPH) technologies have had a particularly prominent role in medical and public health investigations and response; however, these domains are not simply defined and it is difficult to search for relevant information using traditional strategies. To quantify and track the ongoing contributions of GPH to the COVID-19 response, the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention created the COVID-19 Genomics and Precision Health database (COVID-19 GPH), an open access knowledge management system and publications database that is continuously updated through machine learning and manual curation. As of February 11, 2022, COVID-GPH contained 31,597 articles, mostly on pathogen and human genomics (72%). The database also includes articles describing applications of machine learning and artificial intelligence to the investigation and control of COVID-19 (28%). COVID-GPH represents about 10% (22983/221241) of the literature on COVID-19 on PubMed. This unique knowledge management database makes it easier to explore, describe, and track how the pandemic response is accelerating the applications of genomics and precision health technologies. COVID-19 GPH can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action.
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Affiliation(s)
- Wei Yu
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Emily Drzymalla
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Marta Gwinn
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Muin J Khoury
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, GA, USA
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82
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Fang L, Wang K. Multi-label topic classification for COVID-19 literature with Bioformer. ARXIV 2022:arXiv:2204.06758v1. [PMID: 35441084 PMCID: PMC9016643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification problem, where the title is the first sentence, and the abstract is the second sentence. Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task. Compared to the baseline results, our best model increased micro, macro, and instance-based F1 score by 8.8%, 15.5%, 7.4%, respectively. Bioformer achieved the highest micro F1 and macro F1 scores in this challenge. In post-challenge experiments, we found that pretraining of Bioformer on COVID-19 articles further improves the performance.
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Affiliation(s)
- Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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Abstract
Neuroimmunological diseases and their treatment compromise the immune system, thereby increasing the risk of infections and serious illness. Consequently, vaccinations to protect against infections are an important part of the clinical management of these diseases. However, the wide variety of immunotherapies that are currently used to treat neuroimmunological disease — particularly multiple sclerosis and neuromyelitis optica spectrum disorders — can also impair immunological responses to vaccinations. In this Review, we discuss what is known about the effects of various immunotherapies on immunological responses to vaccines and what these effects mean for the safe and effective use of vaccines in patients with a neuroimmunological disease. The success of vaccination in patients receiving immunotherapy largely depends on the specific mode of action of the immunotherapy. To minimize the risk of infection when using immunotherapy, assessment of immune status and exclusion of underlying chronic infections before initiation of therapy are essential. Selection of the required vaccinations and leaving appropriate time intervals between vaccination and administration of immunotherapy can help to safeguard patients. We also discuss the rapidly evolving knowledge of how immunotherapies affect responses to SARS-CoV-2 vaccines and how these effects should influence the management of patients on these therapies during the COVID-19 pandemic. In this Review, the authors discuss how various immunotherapies for neuroimmunological diseases interact with vaccination responses, including responses to SARS-CoV-2 vaccinations, and the implications for the safe and effective use of vaccines in patients with these diseases. Vaccination against infection is an essential part of the management of neuroimmunological diseases. All indicated vaccinations should be administered before initiation of immunotherapy whenever possible; appropriate intervals between vaccination and treatment vary with treatment and vaccination. Inactivated vaccines are considered safe in neuroimmunological diseases but live vaccines are generally contraindicated during immunotherapy. Vaccination responses during immunotherapy can be diminished or abrogated, depending on the treatment and vaccination; antibody titre testing to monitor responses can be considered where appropriate. Vaccinations must be avoided during relapses or exacerbations of neuroimmunological diseases. Vaccination against SARS-CoV-2 is recommended for patients with neuroimmunological disease but some immunotherapies limit the immune response; therefore, timing should be considered carefully.
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Armenta-Medina D, Brambila-Tapia AJL, Miranda-Jiménez S, Rodea-Montero ER. A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder. Diagnostics (Basel) 2022; 12:887. [PMID: 35453935 PMCID: PMC9028729 DOI: 10.3390/diagnostics12040887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 12/10/2022] Open
Abstract
In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.
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Affiliation(s)
- Dagoberto Armenta-Medina
- Consejo Nacional de Ciencia y Tecnología (CONACyT), Ciudad de México 03940, Mexico;
- Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación (INFOTEC), Aguascalientes 20326, Mexico
| | | | - Sabino Miranda-Jiménez
- Consejo Nacional de Ciencia y Tecnología (CONACyT), Ciudad de México 03940, Mexico;
- Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación (INFOTEC), Aguascalientes 20326, Mexico
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Scheinfeld L. LitCovid, iSearch COVID-19 portfolio, and COVID-19 Global literature on coronavirus disease. J Med Libr Assoc 2022; 110:279-280. [PMID: 35440902 PMCID: PMC9014912 DOI: 10.5195/jmla.2022.1274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
LitCovid. National Center for Biotechnology Information, US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894; https://www.ncbi.nlm.nih.gov/research/coronavirus/; free. iSearch COVID-19 portfolio. Office of Portfolio Analysis, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892; https://icite.od.nih.gov/covid19/search/; free. COVID-19 Global literature on coronavirus disease. World Health Organization, Avenue Appia 20, 1211 Geneva; https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/; free.
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Affiliation(s)
- Laurel Scheinfeld
- , Senior Assistant Librarian, Health Sciences Library, Stony Brook University, Stony Brook, NY
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86
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Update of the Potential Treatments for Psychiatric and Neuropsychiatric Symptoms in the Context of the Post-COVID-19 Condition: Still a Lot of Suffering and Many More Things to Learn. TRAUMA CARE 2022. [DOI: 10.3390/traumacare2020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: The World Health Organization (WHO) has defined a post-COVID-19 condition. Some of these symptoms can be categorized as psychiatric long COVID-19 if they appeared in the aftermath of COVID-19, including depression, anxiety, post-traumatic stress disorder, somatic symptoms disorders such as hyperventilation syndrome, fatigue, cognitive and sleep disorders. Psychiatric and neuropsychiatric post-COVID-19 present mental health specialists with difficult challenges because of its complexity and the multiple ways in which it integrates into a singular somatic context. Methods: We conducted a systematic research paradigm from SARS-CoV-2 using LitCOVID and Web of Science to search management strategies and potential treatments for psychiatric post-COVID-19 symptoms. Results: Management strategies must be based on a multidisciplinary approach to promote the global evaluation of psychiatric and physical symptoms, systematic detection and prevention. Selective serotonin reuptake inhibitors appear to be the best choice to treat post-COVID-19 depression and anxiety disorders, and tofisopam could be helpful for anxiety. Cognitive behavioral therapy techniques adjusted to post-COVID-19 fatigue, functional remediation, extracorporeal apheresis, transcutaneous auricular vagus nerve stimulation, monoclonal antibodies, flavonoids, oxytocin or L-carnitine all represent hypothetical therapeutic avenues that remain to be evaluated in clinical trials. Conclusions: Psychiatric and neuropsychiatric post-COVID-19 symptoms occur frequently and are debilitating. Attention should be paid to this condition and studies undertaken to specify the effective treatments.
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Mishra L, Bandyopadhyay T. Unbinding of hACE2 and inhibitors from the receptor binding domain of SARS-CoV-2 spike protein. J Biomol Struct Dyn 2022; 41:3245-3264. [PMID: 35293839 DOI: 10.1080/07391102.2022.2046641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The first direful biomolecular event leading to COVID-19 disease is the SARS-CoV-2 virus surface spike (S) protein-mediated interaction with the human transmembrane protein, angiotensin-converting enzyme 2 (hACE2). Prevention of this interaction presents an attractive alternative to thwart SARS-CoV-2 replications. The development of monoclonal antibodies (mAbs) in the convalescent plasma treatment, nanobody, and designer peptides, which recognizes epitopes that overlap with hACE2 binding sites in the receptor-binding domain (RBD) of S protein (S/RBD) and thereby blocking the infection has been the center stage of therapeutic research. Here we report atomistic and reliable in silico structure-energetic features of the S/RBD interactions with hACE2 and its two inhibitors (convalescent mAb, B38, and an alpaca nanobody, Ty1). The discovered potential of mean forces exhibits free energy basin and barriers along the interaction pathways, providing sufficient molecular insights to design a B38 mutant and a Ty1-based peptide with higher binding capacity. While the mutated B38 forms a 60-fold deeper free energy minimum, the designer peptide (Ty1-based) constitutes 38 amino acids and is found to form a 100-fold deeper free energy minimum in the first binding basin than their wild-type variants in complex with S/RBD. Our strategy may help to design more efficacious biologics towards therapeutic intervention against the current raging pandemic.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lokpati Mishra
- Radiation Safety Systems Division, Bhabha Atomic Research Centre, Mumbai, India
| | - Tusar Bandyopadhyay
- Theoretical Chemistry Section, Chemistry Division, Bhabha Atomic Research Centre, Mumbai, India
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88
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COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments. Pharmaceutics 2022; 14:pharmaceutics14030567. [PMID: 35335943 PMCID: PMC8955179 DOI: 10.3390/pharmaceutics14030567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/25/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022] Open
Abstract
Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.
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89
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Shi W, Fan G, Shen Z, Hu C, Ma J, Zhou Y, Meng Z, Hu S, Bi Y, Wang L, Yu H, Lin S, Sun X, Zhang X, Liu D, Sun Q, Wu L. gcCov: Linked open data for global coronavirus studies. MLIFE 2022; 1:92-95. [PMID: 37731725 PMCID: PMC9088579 DOI: 10.1002/mlf2.12008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 11/25/2021] [Indexed: 09/22/2023]
Abstract
We present a method of mapping data from publicly available genomics and publication resources to the Resource Description Framework (RDF) and implement a server to publish linked open data (LOD). As one of the largest and most comprehensive semantic databases about coronaviruses, the resulted gcCov database demonstrates the capability of using data in the LOD framework to promote correlations between genotypes and phenotypes. These correlations will be helpful for future research on fundamental viral mechanisms and drug and vaccine designs. These LOD with 62,168,127 semantic triplets and their visualizations are freely accessible through gcCov at https://nmdc.cn/gccov/.
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Affiliation(s)
- Wenyu Shi
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Zhihong Shen
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Chuan Hu
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of SciencesBeijingChina
| | - Songnian Hu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Yuhai Bi
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Liang Wang
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Haiying Yu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Siru Lin
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Xiuqiang Sun
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Xinjiao Zhang
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Dongmei Liu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Qinlan Sun
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
- State Key Laboratory of Microbial Resources, Institute of MicrobiologyChinese Academy of SciencesBeijingChina
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90
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Foote SL, Jones S, Lockmuller J, Brown L, Breen J, Gururaj A. Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:466-475. [PMID: 35308924 PMCID: PMC8861695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount of preprint and published literature related to COVID-19 makes it challenging for researchers to stay up to date on research results regarding CoPs against SARS-CoV-2. To address this problem, we developed a machine learning classifier to identify papers relevant to CoPs and a customized named entity recognition (NER) model to extract terms of interest, including CoPs, vaccines, assays, and animal models. A user-friendly visualization tool was populated with the extracted and normalized NER results and associated publication information including links to full-text articles and clinical trial information where available. The goal of this pilot project is to provide a basis for developing real-time informatics platforms that can inform researchers with scientific insights from emerging research.
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Affiliation(s)
- Sydney L Foote
- Office of Data Science and Emerging Technologies, NIAID, NIH, Rockville, MD, USA
- Both authors contributed to the work equally
| | - Sara Jones
- Office of Data Science and Emerging Technologies, NIAID, NIH, Rockville, MD, USA
- Both authors contributed to the work equally
| | - Jane Lockmuller
- Office of Data Science and Emerging Technologies, NIAID, NIH, Rockville, MD, USA
| | - Liliana Brown
- Division of Microbiology and Infectious Diseases, NIAID, NIH, Rockville, MD, USA
| | - Joseph Breen
- Division of Allergy, Immunology, and Transplantation, NIAID, NIH, Rockville, MD, USA
| | - Anupama Gururaj
- Division of Allergy, Immunology, and Transplantation, NIAID, NIH, Rockville, MD, USA
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91
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McLean AR, Rashan S, Tran L, Arena L, Lawal A, Maguire BJ, Adele S, Antonio ES, Brack M, Caldwell F, Carrara VI, Charles R, Citarella BW, Epie TB, Feteh VF, Kennon K, Makuka GJ, Ngu R, Nwosu AP, Obiesie S, Ogbonnaa-Njoku C, Paul P, Richmond C, Singh-Phulgenda S, Strudwick S, Tyrrell CS, Stepniewska K, Strub-Wourgaft N, White NJ, Guérin PJ. The fragmented COVID-19 therapeutics research landscape: a living systematic review of clinical trial registrations evaluating priority pharmacological interventions. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17284.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background: Many available medicines have been evaluated as potential repurposed treatments for coronavirus disease 2019 (COVID-19). We summarise the registered study landscape for 32 priority pharmacological treatments identified following consultation with external experts of the COVID-19 Clinical Research Coalition. Methods: All eligible trial registry records identified by systematic searches of the World Health Organisation International Clinical Trials Registry Platform as of 26th May 2021 were reviewed and extracted. A descriptive summary of study characteristics was performed. Results: We identified 1,314 registered studies that included at least one of the 32 priority pharmacological interventions. The majority (1,043, 79%) were randomised controlled trials (RCTs). The sample size of the RCTs identified was typically small (median (25th, 75th percentile) sample size = 140 patients (70, 383)), i.e. individually powered only to show very large effects. The most extensively evaluated medicine was hydroxychloroquine (418 registered studies). Other widely studied interventions were convalescent plasma (n=208), ritonavir (n=189) usually combined with lopinavir (n=181), and azithromycin (n=147). Very few RCTs planned to recruit participants in low-income countries (n=14; 1.3%). A minority of studies (348, 26%) indicated a willingness to share individual participant data. The living systematic review data are available at https://iddo.cognitive.city Conclusions: There are many registered studies planning to evaluate available medicines as potential repurposed treatments of COVID-19. Most of these planned studies are small, and therefore substantially underpowered for most relevant endpoints. Very few are large enough to have any chance of providing enough convincing evidence to change policies and practices. The sharing of individual participant data (IPD) from these studies would allow pooled IPD meta-analyses which could generate definitive conclusions, but most registered studies did not indicate that they were willing to share their data.
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92
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Vergoulis T, Kanellos I, Chatzopoulos S, Pla Karidi D, Dalamagas T. BIP4COVID19: Releasing impact measures for articles relevant to COVID-19. QUANTITATIVE SCIENCE STUDIES 2022. [DOI: 10.1162/qss_a_00169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Abstract
Since the beginning of the coronavirus pandemic, a large number of relevant articles have been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or even government actions to limit the spread of the disease, and directing treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate, making the exploration of the relevant literature and the identification of useful knowledge challenging. In this work, we describe BIP4COVID19, an open data set that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search interface for COVID-19-related articles, which leverages our data to rank search results according to the calculated impact indicators.
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Affiliation(s)
| | | | - Serafeim Chatzopoulos
- IMSI, “Athena” RC, Athens, Greece
- Dept. of Informatics and Tele/tions, University of the Peloponnese, Tripolis, Greece
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93
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Geronikolou SA, Takan I, Pavlopoulou A, Mantzourani M, Chrousos GP. Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia. Int J Mol Med 2022; 49:35. [PMID: 35059730 PMCID: PMC8815408 DOI: 10.3892/ijmm.2022.5090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/28/2021] [Indexed: 12/16/2022] Open
Abstract
The highly heterogeneous symptomatology and unpredictable progress of COVID-19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS-CoV-2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID-19, as well as in vaccine-induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID-19-mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high-throughput experimental studies, cross-species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID-19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID-19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID-19 and 6.43 in VITT), illustrating the complexity of COVID-19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild-type genes, revealing the predisposition of the body to hypoxia-induced thrombosis, leading to the acute COVID-19 phenotype, the 'long-COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.
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Affiliation(s)
- Styliani A Geronikolou
- Clinical, Translational and Experimental Surgery Research Centre, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
| | - Işil Takan
- Izmir Biomedicine and Genome Center (IBG), 35340 Izmir, Turkey
| | | | - Marina Mantzourani
- First Department of Internal Medicine, Laiko Hospital, National and Kapodistrian University of Athens Medical School, 11527 Athens, Greece
| | - George P Chrousos
- Clinical, Translational and Experimental Surgery Research Centre, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
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94
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Singh R, Rathore SS, Khan H, Bhurwal A, Sheraton M, Ghosh P, Anand S, Makadia J, Ayesha F, Mahapure KS, Mehra I, Tekin A, Kashyap R, Bansal V. Mortality and Severity in COVID-19 Patients on ACEIs and ARBs-A Systematic Review, Meta-Analysis, and Meta-Regression Analysis. Front Med (Lausanne) 2022; 8:703661. [PMID: 35083229 PMCID: PMC8784609 DOI: 10.3389/fmed.2021.703661] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 12/08/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose: The primary objective of this systematic review is to assess association of mortality in COVID-19 patients on Angiotensin-converting-enzyme inhibitors (ACEIs) and Angiotensin-II receptor blockers (ARBs). A secondary objective is to assess associations with higher severity of the disease in COVID-19 patients. Materials and Methods: We searched multiple COVID-19 databases (WHO, CDC, LIT-COVID) for longitudinal studies globally reporting mortality and severity published before January 18th, 2021. Meta-analyses were performed using 53 studies for mortality outcome and 43 for the severity outcome. Mantel-Haenszel odds ratios were generated to describe overall effect size using random effect models. To account for between study results variations, multivariate meta-regression was performed with preselected covariates using maximum likelihood method for both the mortality and severity models. Result: Our findings showed that the use of ACEIs/ARBs did not significantly influence either mortality (OR = 1.16 95% CI 0.94-1.44, p = 0.15, I 2 = 93.2%) or severity (OR = 1.18, 95% CI 0.94-1.48, p = 0.15, I 2 = 91.1%) in comparison to not being on ACEIs/ARBs in COVID-19 positive patients. Multivariate meta-regression for the mortality model demonstrated that 36% of between study variations could be explained by differences in age, gender, and proportion of heart diseases in the study samples. Multivariate meta-regression for the severity model demonstrated that 8% of between study variations could be explained by differences in age, proportion of diabetes, heart disease and study country in the study samples. Conclusion: We found no association of mortality or severity in COVID-19 patients taking ACEIs/ARBs.
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Affiliation(s)
- Romil Singh
- Department of Anesthesiology and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | | | - Hira Khan
- Department of Internal Medicine, Islamic International Medical College, Rawalpindi, Pakistan
| | - Abhishek Bhurwal
- Department of Gastroenterology and Hepatology, Rutgers Robert Wood Johnson School of Medicine, New Brunswick, NJ, United States
| | - Mack Sheraton
- Department of Emergency Medicine, Trinity West Medical Center, Steubenville, OH, United States
| | - Prithwish Ghosh
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Sohini Anand
- Patliputra Medical College and Hospital, Dhanbad, India
| | | | - Fnu Ayesha
- Department of Internal Medicine, Services Institute of Medical Sciences, Lahore, Pakistan
| | - Kiran S. Mahapure
- Department of Plastic Surgery, KAHER J. N. Medical College, Belgaum, India
| | - Ishita Mehra
- Department of Internal Medicine, North Alabama Medical Center, Florence, AL, United States
| | - Aysun Tekin
- Department of Anesthesiology and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Rahul Kashyap
- Department of Anesthesiology and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Vikas Bansal
- Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
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95
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Locas A, Brassard J, Rose-Martel M, Lambert D, Green A, Deckert A, Illing M. Comprehensive Risk Pathway of the Qualitative Likelihood of Human Exposure to Severe Acute Respiratory Syndrome Coronavirus 2 from the Food Chain. J Food Prot 2022; 85:85-97. [PMID: 34499732 PMCID: PMC9906280 DOI: 10.4315/jfp-21-218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/08/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT A group of experts from all Canadian federal food safety partners was formed to monitor the potential issues relating to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) food contamination, to gather and consider all of the relevant evidence and to determine the impact for Canadian food safety. A comprehensive risk pathway was generated to consider the likelihood of a SARS-CoV-2 contamination event at any of the relevant steps of the food processing and handling chain and the potential for exposure and transmission of the virus to the consumer. The scientific evidence was reviewed and assessed for each event in the pathway, taking into consideration relevant elements that could increase or mitigate the risk of contamination. The advantage of having an event-wise contextualization of the SARS-CoV-2 transmission pathway through the food chain is that it provides a systematic and consistent approach to evaluate any new data and communicate its importance and impact. The pathway also increases the objectivity and consistency of the assessment in a rapidly evolving and high-stakes situation. Based on our review and analysis, there is currently no comprehensive epidemiological evidence of confirmed cases of SARS-CoV-2, or its known variants, causing coronavirus disease 2019 from transmission through food or food packaging. Considering the remote possibility of exposure through food, the likelihood of exposure by ingestion or contact with mucosa is considered negligible to very low, and good hygiene practices during food preparation should continue to be followed. HIGHLIGHTS
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Affiliation(s)
- Annie Locas
- Canadian Food Inspection Agency, 1400 Merivale, Ottawa, Ontario, Canada K1A 0Y9,Author for correspondence. Tel: 613-773-6539
| | - Julie Brassard
- Agriculture and Agri-Food Canada, 3600 Casavant Boulevard West, Saint-Hyacinthe, Quebec, Canada J2S 8E3
| | - Megan Rose-Martel
- Health Canada, 251 Sir Frederick Banting Drive, Ottawa, Ontario, Canada K1A 0K9
| | - Dominic Lambert
- Canadian Food Inspection Agency, 3400 Casavant Boulevard West, Saint-Hyacinthe, Quebec, Canada J2S 8E3
| | - Alyssa Green
- Public Health Agency of Canada, 370 Speedvale Avenue West, Guelph, Ontario, Canada N1H 7M7
| | - Anne Deckert
- Public Health Agency of Canada, 370 Speedvale Avenue West, Guelph, Ontario, Canada N1H 7M7
| | - Michelle Illing
- Canadian Food Inspection Agency, 1400 Merivale, Ottawa, Ontario, Canada K1A 0Y9
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96
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Stefanou MI, Palaiodimou L, Bakola E, Smyrnis N, Papadopoulou M, Paraskevas GP, Rizos E, Boutati E, Grigoriadis N, Krogias C, Giannopoulos S, Tsiodras S, Gaga M, Tsivgoulis G. Neurological manifestations of long-COVID syndrome: a narrative review. Ther Adv Chronic Dis 2022; 13:20406223221076890. [PMID: 35198136 PMCID: PMC8859684 DOI: 10.1177/20406223221076890] [Citation(s) in RCA: 100] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/12/2022] [Indexed: 02/05/2023] Open
Abstract
Accumulating evidence points toward a very high prevalence of prolonged neurological symptoms among coronavirus disease 2019 (COVID-19) survivors. To date, there are no solidified criteria for 'long-COVID' diagnosis. Nevertheless, 'long-COVID' is conceptualized as a multi-organ disorder with a wide spectrum of clinical manifestations that may be indicative of underlying pulmonary, cardiovascular, endocrine, hematologic, renal, gastrointestinal, dermatologic, immunological, psychiatric, or neurological disease. Involvement of the central or peripheral nervous system is noted in more than one-third of patients with antecedent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, while an approximately threefold higher incidence of neurological symptoms is recorded in observational studies including patient-reported data. The most frequent neurological manifestations of 'long-COVID' encompass fatigue; 'brain fog'; headache; cognitive impairment; sleep, mood, smell, or taste disorders; myalgias; sensorimotor deficits; and dysautonomia. Although very limited evidence exists to date on the pathophysiological mechanisms implicated in the manifestation of 'long-COVID', neuroinflammatory and oxidative stress processes are thought to prevail in propagating neurological 'long-COVID' sequelae. In this narrative review, we sought to present a comprehensive overview of our current understanding of clinical features, risk factors, and pathophysiological processes of neurological 'long-COVID' sequelae. Moreover, we propose diagnostic and therapeutic algorithms that may aid in the prompt recognition and management of underlying causes of neurological symptoms that persist beyond the resolution of acute COVID-19. Furthermore, as causal treatments for 'long-COVID' are currently unavailable, we propose therapeutic approaches for symptom-oriented management of neurological 'long-COVID' symptoms. In addition, we emphasize that collaborative research initiatives are urgently needed to expedite the development of preventive and therapeutic strategies for neurological 'long-COVID' sequelae.
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Affiliation(s)
- Maria-Ioanna Stefanou
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Lina Palaiodimou
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Eleni Bakola
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos Smyrnis
- Second Department of Psychiatry, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Marianna Papadopoulou
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece; Department of Physiotherapy, University of West Attica, Athens, Greece
| | - George P. Paraskevas
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Emmanouil Rizos
- Second Department of Psychiatry, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Eleni Boutati
- Second Propaedeutic Department of Internal Medicine and Research Institute, University General Hospital Attikon, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos Grigoriadis
- Second Department of Neurology, ‘AHEPA’ University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christos Krogias
- Department of Neurology, St. Josef-Hospital Bochum, Ruhr University Bochum, Bochum, Germany
| | - Sotirios Giannopoulos
- Second Department of Neurology, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sotirios Tsiodras
- 4th Department of Internal Medicine, School of Medicine, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Mina Gaga
- 7th Respiratory Medicine Department and Asthma Center, Athens Chest Hospital ‘Sotiria’, Athens, Greece
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97
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Guerrier C, McDonnell C, Magoc T, Fishe JN, Harle CA. Understanding Health Care Administrators’ Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health System. MDM Policy Pract 2022; 7:23814683221089844. [PMID: 35368410 PMCID: PMC8972941 DOI: 10.1177/23814683221089844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Abstract
Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.
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Affiliation(s)
- Christina Guerrier
- Center for Data Solutions, University of Florida Health Science Center, Jacksonville, Florida, USA
| | - Cara McDonnell
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Tanja Magoc
- Integrated Data Repository, University of Florida, Gainesville, Florida, USA
| | - Jennifer N. Fishe
- Center for Data Solutions, University of Florida Health Science Center, Jacksonville, Florida, USA
| | - Christopher A. Harle
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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98
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Salter-Volz AE, Oyasu A, Yeh C, Muhammad LN, Woitowich NC. Sex and Gender Bias in Covid-19 Clinical Case Reports. Front Glob Womens Health 2021; 2:774033. [PMID: 34881381 PMCID: PMC8647159 DOI: 10.3389/fgwh.2021.774033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022] Open
Abstract
Clinical case reports circulate relevant information regarding disease presentation and describe treatment protocols, particularly for novel conditions. In the early months of the Covid-19 pandemic, case reports provided key insights into the pathophysiology and sequelae associated with Covid-19 infection and described treatment mechanisms and outcomes. However, case reports are often subject to selection bias due to their singular nature. To better understand how selection biases may have influenced Covid-19-releated case reports, we conducted a bibliometric analysis of Covid-19-releated case reports published in high impact journals from January 1 to June 1, 2020. Case reports were coded for patient sex, country of institutional affiliation, physiological system, and first and last author gender. Of 494 total case reports, 45% (n = 221) of patients were male, 30% (n = 146) were female, and 25% (n = 124) included both sexes. Ratios of male-only to female-only case reports varied by physiological system. The majority of case reports had male first (61%, n = 302) and last (70%, n = 340) authors. Case reports with male last authors were more likely to describe male patients [X2 (2, n = 465) = 6.6, p = 0.037], while case reports with female last authors were more likely to include patients of both sexes [OR = 1.918 (95% CI = 1.163–3.16)]. Despite a limited sample size, these data reflect emerging research on sex-differences in the physiological presentation and impact of Covid-19 and parallel large-scale trends in authorship patterns. Ultimately, this work highlights potential biases in the dissemination of clinical information via case reports and underscores the inextricable influences of sex and gender biases within biomedicine.
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Affiliation(s)
- Aysha E Salter-Volz
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Abigail Oyasu
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.,University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Chen Yeh
- Division of Biostatistics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Lutfiyya N Muhammad
- Division of Biostatistics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nicole C Woitowich
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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99
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Zheng K, Ortner P, Lim YW, Zhi TJ. Ventilation in worker dormitories and its impact on the spread of respiratory droplets. SUSTAINABLE CITIES AND SOCIETY 2021; 75:103327. [PMID: 34545319 PMCID: PMC8443870 DOI: 10.1016/j.scs.2021.103327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/31/2021] [Accepted: 08/31/2021] [Indexed: 05/29/2023]
Abstract
Most of the COVID-19 cases in Singapore have primarily come from foreign worker dormitories. This people group is especially vulnerable partly because of behavioural habits, but the built environment they live in also plays a significant role. These dormitories are typically densely populated, so the living conditions are cramped. The short lease given to most dormitories also means the design does not typically focus on environmental performance, like good natural ventilation. This paper seeks to understand how these dormitories' design affects natural ventilation and, subsequently, the spread of the COVID-19 particles by looking at two existing worker dorms in Singapore. Findings show that some rooms are poorly orientated against the prevailing wind directions, so there is dominant stagnant air in these rooms, leading to respiratory droplets' long residence times. These particles can hover in the air for 10 min and more. Interventions like increased bed distance and removing upper deck beds only showed limited ventilation improvements in some rooms. Comparatively, internal wind scoops' strategic placement was more effective at directing wind towards more stagnant zones. Large canyon aspect ratios were also effective at removing particles from higher elevations.
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Affiliation(s)
- Kai Zheng
- Architecture and Sustainable Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
| | - Peter Ortner
- Architecture and Sustainable Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
| | - Yu Wen Lim
- Architecture and Sustainable Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
| | - Tay Jing Zhi
- Architecture and Sustainable Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
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100
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Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, Connor R, Funk K, Kelly C, Kim S, Madej T, Marchler-Bauer A, Lanczycki C, Lathrop S, Lu Z, Thibaud-Nissen F, Murphy T, Phan L, Skripchenko Y, Tse T, Wang J, Williams R, Trawick BW, Pruitt KD, Sherry ST. Database resources of the national center for biotechnology information. Nucleic Acids Res 2021; 50:D20-D26. [PMID: 34850941 DOI: 10.1093/nar/gkab1112] [Citation(s) in RCA: 828] [Impact Index Per Article: 276.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/20/2021] [Accepted: 11/18/2021] [Indexed: 11/14/2022] Open
Abstract
The National Center for Biotechnology Information (NCBI) produces a variety of online information resources for biology, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for the most of these databases. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, RefSeq, SRA, Virus, dbSNP, dbVar, ClinicalTrials.gov, MMDB, iCn3D and PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.
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Affiliation(s)
- Eric W Sayers
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - J Rodney Brister
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathi Canese
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jessica Chan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Donald C Comeau
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Ryan Connor
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathryn Funk
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Chris Kelly
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tom Madej
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Aron Marchler-Bauer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Christopher Lanczycki
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stacy Lathrop
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Francoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Terence Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Lon Phan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Yuri Skripchenko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tony Tse
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jiyao Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Rebecca Williams
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Barton W Trawick
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
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